Central line shifting based pre-change lane path planning

ABSTRACT

A method, apparatus, and system for executing a lane change is disclosed. That an ADV is in a lane changing region for an anticipated lane change into a neighboring target lane in a first direction is determined. The first direction is either a left direction or a right direction in a direction of travel of the ADV. A reference line is moved toward the target lane in the first direction at a predetermined rate while the reference line is kept within a current lane of the ADV. A gap in traffic for the anticipated lane change is searched for. Thereafter, in response to finding the gap in traffic for the anticipated lane change, the ADV is controlled to complete the lane change into the target lane.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to path planning for a lane change.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Planning and executing a lane change for an autonomous vehicle can be achallenging task, especially when the autonomous vehicle needs to bemerged into traffic in the target lane. The difficulties associated withthe search for a gap in traffic for the lane change are exacerbated whenthe intention of the autonomous vehicle to change the lane is notadequately signaled to the vehicles in the target lane.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 is a diagram illustrating an example environment in whichembodiments of the disclosure may be practiced.

FIG. 5 is a flowchart illustrating an example method for executing alane change according to one embodiment.

FIG. 6 is a block diagram illustrating various modules that can be usedin embodiments.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, the reference line of an autonomousdriving vehicle (ADV) is gradually moved toward the target lane for ananticipated lane change while being kept within the current lane of theADV prior to a gap in traffic is found for the execution of the lanechange. Accordingly, traveling forward, the ADV also moves laterallytoward the boundary of the current lane in the direction of theanticipated lane change before the lane change is actually executed. Thelateral in-lane motion of the ADV prior to the lane change serves tosignal the anticipated lane change to (the drivers of) the vehicles inthe target lane, and also reduces the lateral distance that needs to betraversed when the lane change is executed. The signaling as well as thelateral in-lane motion toward the target lane makes it more likely thata gap in traffic can be successfully found for the execution of the lanechange.

In particular, that an ADV is in a lane changing region for ananticipated lane change into a neighboring target lane in a firstdirection is determined. A reference line is moved toward the targetlane in the first direction at a predetermined rate while the referenceline is kept within a current lane of the ADV. Further trajectory orpath to drive the ADV is planned based on the modified reference line tocontrol the ADV to move towards the lane boundary between the currentlane and the target lane in preparing the lane changing. A gap intraffic for the anticipated lane change is searched for. Thereafter, inresponse to finding the gap in traffic for the anticipated lane change,the ADV is controlled to complete the lane change into the target lane.

In one embodiment, the first direction is either a left direction or aright direction in a direction of travel of the ADV. In one embodiment,the predetermined rate is between 0.1 m/s and 0.5 m/s in a lateralspeed. In one embodiment, moving the reference line toward the targetlane in the first direction at the predetermined rate comprisesdeflecting the reference line toward the target lane away from adirection of the current lane by a predetermined deflection angle. Inone embodiment, the moving of the reference line toward the target lanein the first direction is subject to a predetermined maximum lateralspeed. In one embodiment, prior to finding the gap in traffic for theanticipated lane change, the reference line is moved toward the targetlane in the first direction until a distance between the reference lineand a boundary of the current lane of the ADV is at or below apredetermined threshold such as 0.2 meters. However, such a thresholdmay be determined in view of the vehicle width of the ADV, such that thebody of the vehicle does not enter or occupy the target lane inpreparing the lane changing.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. Algorithms 124 may include an algorithm to modify areference line in preparing for lane changing to provide an indicationto a vehicle in the target lane that an ADV is about to change lane, aswell as an algorithm to determine a lane changing region. Algorithms 124can then be uploaded on ADVs to be utilized during autonomous driving inreal-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, and lane change module 308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

In one embodiment, lane changing module 308 is responsible for handlinglane changing driving scenario based on a set of one or more lanechanging rules 313. Lane changing module 308 may be integrated withplanning module 305. When a request for lane changing is received, forexample, from decision module 304, lane changing module 308 is to modifythe reference line of the current lane gradually towards, according to apredetermined formula, to a lane boundary between the current lane and atarget lane to which the ADV is to change. Based on the modifiedreference line, a trajectory or a path is planned, for example, byplanning module 305 to control the ADV to move within the current lanebut towards to the target lane in preparing lane changing but before theactual lane changing. Such a move towards the lane boundary will providean indication to other vehicles in the target lane that the ADV intendsto change lane from the current lane to the target lane. Other vehiclesmay either slow down or accelerate to provide a large enough space forthe ADV to change lane.

The reference line is repeatedly modified in each planning cycle untilthe ADV is within a predetermined distance with the target lane whilemaintaining the ADV within the current lane. Meanwhile the system looksfor a gap or lane changing space to change lane. If there is no bigenough gap available for lane changing, the ADV may be maintained withinthe current lane but close to the lane boundary representing anintention of lane changing to the other vehicles in target lane and thecurrent lane. In one embodiment, a gap for lane changing may bedetermined based on the driving environment at the point in time, suchas, for example, the current speed of ADV and other vehicles moving inthe target lane. In one embodiment, a gap that is big enough for lanechanging is a period of time in which the ADV will not collide with thevehicles in the target lane when the lane changing occurs, if the ADVand other vehicles were moving at their current speeds. In a particularembodiment, the period of time is at least 8 seconds.

Referring to FIG. 4, a diagram illustrating an example environment 400in which embodiments of the disclosure may be practiced is shown. TheADV 410 anticipates a lane change into the target lane 440 to the rightin the direction of travel. That the ADV 410 is in a lane changingregion for the anticipated rightward lane change into the target lane440 is determined. The information about the lane changing region isavailable at one or more of: routing module 307, decision module 304, orplanning module 305. For example, decision module 304 may at certainpoint that there is a need to change lane for the ADV given the currentdriving environment. A lane changing region refers to a region withinwhich a vehicle can legally change lane according to the traffic rules.For example, if a lane line between two adjacent lanes is a broken ordash line, a vehicle is allowed to change lane. Such lane changingregion information can be provided by routing module 307 in view of themap or other traffic information. Alternatively, it can also bedetermined based on perception, for example, by recognizing the laneline patterns based on images, etc.

At the segment 430A, the reference line 430 is moved or modifiedrightwardly towards the target lane 440 at a predetermined rate whilethe reference line 430 is kept within a current lane 442 of the ADV 410.The value of the predetermined rate does not limit the disclosure. Forexample, the predetermined rate can be between 0.1 m/s and 0.5 m/s inthe lateral speed. In one embodiment, the predetermined rate is 0.3 m/s(i.e., if there are 10 planning cycles in one second, the reference lineis moved laterally by 0.03 m in each cycle).

In one embodiment, moving the reference line 430 rightwardly towards thetarget lane 440 at the predetermined rate comprises deflecting thereference line 430 toward the target lane 440 away from a direction ofthe current lane 442 by a predetermined deflection angle. Themeasurement of the deflection angle does not limit the disclosure. Inone embodiment, the deflection angle may be 3° (i.e., the lateral speedin this case equals to tan 3°*the speed in the lane direction). In oneembodiment, the rightward moving of the reference line 430 toward thetarget lane 440 is subject to a predetermined maximum lateral speed(i.e., 3 m/s). It should be appreciated that the predetermined maximumlateral speed does not limit the disclosure.

In one embodiment, prior to finding the gap in traffic for theanticipated lane change, the reference line 430 is moved rightwardlytowards the target lane 440 until a distance between the reference line430 and the right boundary of the current lane 442 of the ADV 410 is ator below a predetermined threshold. The value of the predeterminedthreshold does not limit the disclosure. In one embodiment, thepredetermined threshold may be 0.2 m. However, such a threshold may bedetermined in view of the vehicle width of the ADV, such that the bodyof the vehicle does not enter or occupy the target lane in preparing thelane changing. Maintaining a safe distance between the reference line430 and the boundary of the current lane 442 ensures that the ADV 410stays within the current lane 442 and does not intrude into theneighboring lane before the lane change can be safely executed.

Accordingly, traveling forward, the ADV 410 also moves laterally to theright toward the boundary of the current lane 442 in the direction ofthe anticipated lane change before the lane change is actually executed.The rightward lateral in-lane motion of the ADV 410 prior to the lanechange serves to signal the anticipated lane change to (the drivers of)the vehicles (e.g., vehicle 420) in the target lane 440, and alsoreduces the lateral distance that needs to be traversed by the ADV 410when the lane change is executed.

A gap in traffic for the anticipated lane change is searched for. Thesignaling as well as the lateral in-lane motion toward the target lane440 makes it more likely that a gap in traffic can be found for theexecution of the lane change. Thereafter, corresponding to segment 430Bon the reference line 430, in response to finding the gap in traffic forthe anticipated lane change, the ADV 410 is controlled by planningmodule 305 and control module 306 to complete the lane change into thetarget lane 440.

In another embodiment, the ADV 410 is controlled to execute a leftwardlane change. It should be appreciated that although a rightward lanechange is illustrated in FIG. 4 and described, it is within the skillsof a person of ordinary skill in the art to make necessary adaptationsof the description for embodiments associated with a leftward lanechange.

Referring to FIG. 5, a flowchart illustrating an example method 500 forexecuting a lane change according to one embodiment is shown. The method500 can be implemented in hardware, software, or a combination thereof.At block 510, that an ADV is in a lane changing region for ananticipated lane change into a neighboring target lane in a firstdirection is determined. The first direction is either a left directionor a right direction in a direction of travel of the ADV. At block 520,a reference line is moved toward the target lane in the first directionat a predetermined rate while the reference line is kept within acurrent lane of the ADV. A trajectory or path is then planned based onthe modified reference line such that the ADV moves within the currentlane but towards the lane boundary between the current lane and thetarget lane. At block 530, a gap in traffic for the anticipated lanechange is searched for. Thereafter, at block 540, in response to findingthe gap in traffic for the anticipated lane change, the ADV iscontrolled to complete the lane change into the target lane.

Referring to FIG. 6, a block diagram 600 illustrating various modulesthat can be used in embodiments is shown. The modules 610-640 can beimplemented in hardware, software, or a combination thereof. Modules610-640 may be implemented as a part of lane changing module 308. A lanechanging region determining module 610 determines that an ADV is in alane changing region for an anticipated lane change into a neighboringtarget lane in a first direction. A lane changing region refers to aregion within which a vehicle can legally change lane according to thetraffic rules. For example, if a lane line between two adjacent lanes isa broken or dash line, a vehicle is allowed to change lane. Such lanechanging region information can be provided by routing module 307 inview of the map or other traffic information. Alternatively, it can alsobe determined based on perception, for example, by recognizing the laneline patterns based on images, etc. The first direction is either a leftdirection or a right direction in a direction of travel of the ADV. Areference line moving module 620 moves a reference line toward thetarget lane in the first direction at a predetermined rate while thereference line is kept within a current lane of the ADV. A gap searchingmodule 630 searches for a gap in traffic for the anticipated lanechange. An ADV control module 640 controls, in response to finding thegap in traffic for the anticipated lane change, the ADV to complete thelane change into the target lane.

Therefore, according to embodiments of the disclosure, in anticipationof a lane change, the ADV, while traveling forward, also moves laterallytoward the boundary of the current lane in the direction of theanticipated lane change before the lane change is actually executed. Thelateral in-lane motion of the ADV prior to the lane change serves tosignal the anticipated lane change to (the drivers of) the vehicles inthe target lane, and also reduces the lateral distance that needs to betraversed when the lane change is executed. The signaling as well as thelateral in-lane motion toward the target lane makes it more likely thata gap in traffic can be successfully found for the safe execution of thelane change.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle, the method comprising: determining that anautonomous driving vehicle (ADV) moving in a current lane is in a lanechanging region for an anticipated lane change into a target laneadjacent to the current lane in a first direction; modifying a referenceline of the current lane towards the target lane in the first directionat a predetermined rate while keeping the reference line within thecurrent lane of the ADV; planning a trajectory based on the modifiedreference line to drive the ADV towards a lane boundary between thecurrent lane and the target lane, while keeping the ADV within thecurrent lane; searching for a gap in traffic between the ADV and one ormore moving obstacles moving within the target lane for the anticipatedlane change; and in response to finding the gap in traffic for theanticipated lane change, controlling the ADV to complete the lane changeinto the target lane.
 2. The method of claim 1, wherein the firstdirection is either a left direction or a right direction in a directionof travel of the ADV.
 3. The method of claim 1, wherein thepredetermined rate is between 0.1 meters per second (m/s) and 0.5 m/s ina lateral speed.
 4. The method of claim 1, wherein modifying thereference line of the current lane towards the target lane in the firstdirection at the predetermined rate comprises deflecting the referenceline towards the target lane away from a direction of the current laneby a predetermined deflection angle.
 5. The method of claim 4, whereinmodifying the reference line towards the target lane in the firstdirection is subject to a predetermined maximum lateral speed.
 6. Themethod of claim 1, wherein prior to finding the gap in traffic for theanticipated lane change, the reference line is moved towards the targetlane in the first direction until a distance between the reference lineand the lane boundary of the current lane of the ADV is at or below apredetermined threshold.
 7. The method of claim 6, wherein thepredetermined threshold is approximately 0.2 meters.
 8. A non-transitorymachine-readable medium having instructions stored therein, which whenexecuted by a processor, cause the processor to perform operations, theoperations comprising: determining that an autonomous driving vehicle(ADV) moving in a current lane is in a lane changing region for ananticipated lane change into a target lane adjacent to the current lanein a first direction; modifying a reference line of the current lanetowards the target lane in the first direction at a predetermined ratewhile keeping the reference line within the current lane of the ADV;planning a trajectory based on the modified reference line to drive theADV towards a lane boundary between the current lane and the targetlane, while keeping the ADV within the current lane; searching for a gapin traffic between the ADV and one or more moving obstacles movingwithin the target lane for the anticipated lane change; and in responseto finding the gap in traffic for the anticipated lane change,controlling the ADV to complete the lane change into the target lane. 9.The machine-readable medium of claim 8, wherein the first direction iseither a left direction or a right direction in a direction of travel ofthe ADV.
 10. The machine-readable medium of claim 8, wherein thepredetermined rate is between 0.1 meters per second (m/s) and 0.5 m/s ina lateral speed.
 11. The machine-readable medium of claim 8, whereinmodifying the reference line of the current lane towards the target lanein the first direction at the predetermined rate comprises deflectingthe reference line towards the target lane away from a direction of thecurrent lane by a predetermined deflection angle.
 12. Themachine-readable medium of claim 11, wherein modifying the referenceline towards the target lane in the first direction is subject to apredetermined maximum lateral speed.
 13. The machine-readable medium ofclaim 8, wherein prior to finding the gap in traffic for the anticipatedlane change, the reference line is moved towards the target lane in thefirst direction until a distance between the reference line and the laneboundary of the current lane of the ADV is at or below a predeterminedthreshold.
 14. The machine-readable medium of claim 13, wherein thepredetermined threshold is approximately 0.2 meters.
 15. A dataprocessing system, comprising: a processor; and a memory coupled to theprocessor to store instructions, which when executed by the processor,cause the processor to perform operations, the operations including:determining that an autonomous driving vehicle (ADV) moving in a currentlane is in a lane changing region for an anticipated lane change into atarget lane adjacent to the current lane in a first direction, modifyinga reference line of the current lane towards the target lane in thefirst direction at a predetermined rate while keeping the reference linewithin the current lane of the ADV, planning a trajectory based on themodified reference line to drive the ADV towards a lane boundary betweenthe current lane and the target lane, while keeping the ADV within thecurrent lane, searching for a gap in traffic between the ADV and one ormore moving obstacles moving within the target lane for the anticipatedlane change, and in response to finding the gap in traffic for theanticipated lane change, controlling the ADV to complete the lane changeinto the target lane.
 16. The system of claim 15, wherein the firstdirection is either a left direction or a right direction in a directionof travel of the ADV.
 17. The system of claim 15, wherein thepredetermined rate is between 0.1 meters per second (m/s) and 0.5 m/s ina lateral speed.
 18. The system of claim 15, wherein modifying thereference line of the current lane towards the target lane in the firstdirection at the predetermined rate comprises deflecting the referenceline towards the target lane away from a direction of the current laneby a predetermined deflection angle.
 19. The system of claim 18, whereinmodifying the reference line towards the target lane in the firstdirection is subject to a predetermined maximum lateral speed.
 20. Thesystem of claim 15, wherein prior to finding the gap in traffic for theanticipated lane change, the reference line is moved towards the targetlane in the first direction until a distance between the reference lineand the lane boundary of the current lane of the ADV is at or below apredetermined threshold.
 21. The system of claim 20, wherein thepredetermined threshold is approximately 0.2 meters.